了解多处理：Python中的共享内存管理，锁和队列

Question

多处理是python中一个强大的工具，我想更深入地理解它。 我想知道何时使用常规 锁和队列以及何时使用多处理管理器在所有进程之间共享这些。

我提出了以下测试场景，其中包含四种不同的多处理条件：

1. 使用池和NO管理器

2. 使用池和管理器

3. 使用单个流程和NO Manager

4. 使用单个进程和Manager

工作

所有条件都执行作业函数the_job 。 the_job包含一些由锁定保护的打印。 此外，函数的输入只是放入队列（以查看它是否可以从队列中恢复）。 此输入只是在主脚本中创建的range(10)的索引idx ，名为start_scenario （显示在底部）。

def the_job(args):
    """The job for multiprocessing.

    Prints some stuff secured by a lock and 
    finally puts the input into a queue.

    """
    idx = args[0]
    lock = args[1]
    queue=args[2]

    lock.acquire()
    print 'I'
    print 'was '
    print 'here '
    print '!!!!'
    print '1111'
    print 'einhundertelfzigelf\n'
    who= ' By run %d \n' % idx
    print who
    lock.release()

    queue.put(idx)

条件的成功定义为完全从队列中调用输入，请参见底部的函数read_queue 。

条件

条件1和2是相当不言自明的。 条件1涉及创建锁和队列，并将这些传递给进程池：

def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    FAILS!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    mypool.imap(jobfunc, iterator)

    mypool.close()
    mypool.join()

    return read_queue(queue)

（辅助函数make_iterator在本文的底部给出。）条件1因RuntimeError: Lock objects should only be shared between processes through inheritance失败RuntimeError: Lock objects should only be shared between processes through inheritance 。

条件2非常相似，但现在锁和队列在经理的监督下：

def scenario_2_pool_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITH a Manager for the lock and queue.

    SUCCESSFUL!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)
    mypool.imap(jobfunc, iterator)
    mypool.close()
    mypool.join()

    return read_queue(queue)

在条件3中，手动启动新进程，并在没有管理器的情况下创建锁和队列：

def scenario_3_single_processes_no_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITHOUT a Manager,

    SUCCESSFUL!

    """
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)

条件4类似但现在再次使用经理：

def scenario_4_single_processes_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITH a Manager,

    SUCCESSFUL!

    """
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)

在这两个条件中 - 3和4 - 我为the_job的10个任务中的每个任务启动一个新进程，其中至多ncores进程同时运行。 这是通过以下辅助函数实现的：

def do_job_single_processes(jobfunc, iterator, ncores):
    """Runs a job function by starting individual processes for every task.

    At most `ncores` processes operate at the same time

    :param jobfunc: Job to do

    :param iterator:

        Iterator over different parameter settings,
        contains a lock and a queue

    :param ncores:

        Number of processes operating at the same time

    """
    keep_running=True
    process_dict = {} # Dict containing all subprocees

    while len(process_dict)>0 or keep_running:

        terminated_procs_pids = []
        # First check if some processes did finish their job
        for pid, proc in process_dict.iteritems():

            # Remember the terminated processes
            if not proc.is_alive():
                terminated_procs_pids.append(pid)

        # And delete these from the process dict
        for terminated_proc in terminated_procs_pids:
            process_dict.pop(terminated_proc)

        # If we have less active processes than ncores and there is still
        # a job to do, add another process
        if len(process_dict) < ncores and keep_running:
            try:
                task = iterator.next()
                proc = mp.Process(target=jobfunc,
                                                   args=(task,))
                proc.start()
                process_dict[proc.pid]=proc
            except StopIteration:
                # All tasks have been started
                keep_running=False

        time.sleep(0.1)

结果

只有条件1失败（ RuntimeError: Lock objects should only be shared between processes through inheritance ），而其他3个条件成功。 我试图围绕这个结果。

为什么池需要在所有进程之间共享锁和队列，但条件3中的各个进程不需要？

我所知道的是，对于池条件（1和2），来自迭代器的所有数据都通过酸洗传递，而在单个进程条件（3和4）中，来自迭代器的所有数据都是通过主进程的继承传递的（我是使用Linux ）。 我想直到从子进程内部更改内存，访问父进程使用的相同内存（写时复制）。 但是只要一个人说lock.acquire() ，就应该改变它，并且子进程确实使用放在内存中其他地方的不同锁，不是吗？ 一个子进程如何知道兄弟已经激活了一个不通过管理员共享的锁？

最后，有点相关的是我的问题，有多少不同的条件3和4。 两者都有单独的流程，但它们在经理的使用上有所不同。 两者都被认为是有效的代码吗？ 或者，如果实际上不需要经理，应该避免使用经理吗？

---

完整的脚本

对于那些只想复制并粘贴所有代码以执行代码的人来说，这里是完整的脚本：

__author__ = 'Me and myself'

import multiprocessing as mp
import time

def the_job(args):
    """The job for multiprocessing.

    Prints some stuff secured by a lock and 
    finally puts the input into a queue.

    """
    idx = args[0]
    lock = args[1]
    queue=args[2]

    lock.acquire()
    print 'I'
    print 'was '
    print 'here '
    print '!!!!'
    print '1111'
    print 'einhundertelfzigelf\n'
    who= ' By run %d \n' % idx
    print who
    lock.release()

    queue.put(idx)


def read_queue(queue):
    """Turns a qeue into a normal python list."""
    results = []
    while not queue.empty():
        result = queue.get()
        results.append(result)
    return results


def make_iterator(args, lock, queue):
    """Makes an iterator over args and passes the lock an queue to each element."""
    return ((arg, lock, queue) for arg in args)


def start_scenario(scenario_number = 1):
    """Starts one of four multiprocessing scenarios.

    :param scenario_number: Index of scenario, 1 to 4

    """
    args = range(10)
    ncores = 3
    if scenario_number==1:
        result =  scenario_1_pool_no_manager(the_job, args, ncores)

    elif scenario_number==2:
        result =  scenario_2_pool_manager(the_job, args, ncores)

    elif scenario_number==3:
        result =  scenario_3_single_processes_no_manager(the_job, args, ncores)

    elif scenario_number==4:
        result =  scenario_4_single_processes_manager(the_job, args, ncores)

    if result != args:
        print 'Scenario %d fails: %s != %s' % (scenario_number, args, result)
    else:
        print 'Scenario %d successful!' % scenario_number


def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    FAILS!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    mypool.map(jobfunc, iterator)

    mypool.close()
    mypool.join()

    return read_queue(queue)


def scenario_2_pool_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITH a Manager for the lock and queue.

    SUCCESSFUL!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)
    mypool.map(jobfunc, iterator)
    mypool.close()
    mypool.join()

    return read_queue(queue)


def scenario_3_single_processes_no_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITHOUT a Manager,

    SUCCESSFUL!

    """
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)


def scenario_4_single_processes_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITH a Manager,

    SUCCESSFUL!

    """
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)


def do_job_single_processes(jobfunc, iterator, ncores):
    """Runs a job function by starting individual processes for every task.

    At most `ncores` processes operate at the same time

    :param jobfunc: Job to do

    :param iterator:

        Iterator over different parameter settings,
        contains a lock and a queue

    :param ncores:

        Number of processes operating at the same time

    """
    keep_running=True
    process_dict = {} # Dict containing all subprocees

    while len(process_dict)>0 or keep_running:

        terminated_procs_pids = []
        # First check if some processes did finish their job
        for pid, proc in process_dict.iteritems():

            # Remember the terminated processes
            if not proc.is_alive():
                terminated_procs_pids.append(pid)

        # And delete these from the process dict
        for terminated_proc in terminated_procs_pids:
            process_dict.pop(terminated_proc)

        # If we have less active processes than ncores and there is still
        # a job to do, add another process
        if len(process_dict) < ncores and keep_running:
            try:
                task = iterator.next()
                proc = mp.Process(target=jobfunc,
                                                   args=(task,))
                proc.start()
                process_dict[proc.pid]=proc
            except StopIteration:
                # All tasks have been started
                keep_running=False

        time.sleep(0.1)


def main():
    """Runs 1 out of 4 different multiprocessing scenarios"""
    start_scenario(1)


if __name__ == '__main__':
    main()

Answer 1

multiprocessing.Lock是使用OS提供的Semaphore对象实现的。 在Linux上，子os.fork通过os.fork从父os.fork继承了Semaphore的os.fork 。 这不是信号量的副本; 它实际上是继承父项所具有的相同句柄，与继承文件描述符的方式相同。 另一方面，Windows不支持os.fork ，所以它必须腌制Lock 。 它通过使用Windows DuplicateHandle API创建一个由multiprocessing.Lock对象在内部使用的Windows信号量的重复句柄来实现这一点，该API指出：

重复句柄引用与原始句柄相同的对象。 因此，对象的任何更改都通过两个句柄反映出来

DuplicateHandle API允许您将重复句柄的所有权赋予子进程，以便子进程在取消对其进行实际使用后可以使用它。 通过创建子项拥有的重复句柄，您可以有效地“共享”锁定对象。

这是multiprocessing/synchronize.py的信号量对象

class SemLock(object):

    def __init__(self, kind, value, maxvalue):
        sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue)
        debug('created semlock with handle %s' % sl.handle)
        self._make_methods()

        if sys.platform != 'win32':
            def _after_fork(obj):
                obj._semlock._after_fork()
            register_after_fork(self, _after_fork)

    def _make_methods(self):
        self.acquire = self._semlock.acquire
        self.release = self._semlock.release
        self.__enter__ = self._semlock.__enter__
        self.__exit__ = self._semlock.__exit__

    def __getstate__(self):  # This is called when you try to pickle the `Lock`.
        assert_spawning(self)
        sl = self._semlock
        return (Popen.duplicate_for_child(sl.handle), sl.kind, sl.maxvalue)

    def __setstate__(self, state): # This is called when unpickling a `Lock`
        self._semlock = _multiprocessing.SemLock._rebuild(*state)
        debug('recreated blocker with handle %r' % state[0])
        self._make_methods()

注意__getstate__的assert_spawning调用，它在pickle对象时被调用。 这是如何实现的：

#
# Check that the current thread is spawning a child process
#

def assert_spawning(self):
    if not Popen.thread_is_spawning():
        raise RuntimeError(
            '%s objects should only be shared between processes'
            ' through inheritance' % type(self).__name__
            )

该函数是通过调用thread_is_spawning确保您“继承” Lock函数。 在Linux上，该方法只返回False ：

@staticmethod
def thread_is_spawning():
    return False

这是因为Linux不需要pickle来继承Lock ，所以如果在Linux上实际调用__getstate__ ，我们就不能继承。 在Windows上，还有更多内容：

def dump(obj, file, protocol=None):
    ForkingPickler(file, protocol).dump(obj)

class Popen(object):
    '''
    Start a subprocess to run the code of a process object
    '''
    _tls = thread._local()

    def __init__(self, process_obj):
        ...
        # send information to child
        prep_data = get_preparation_data(process_obj._name)
        to_child = os.fdopen(wfd, 'wb')
        Popen._tls.process_handle = int(hp)
        try:
            dump(prep_data, to_child, HIGHEST_PROTOCOL)
            dump(process_obj, to_child, HIGHEST_PROTOCOL)
        finally:
            del Popen._tls.process_handle
            to_child.close()


    @staticmethod
    def thread_is_spawning():
        return getattr(Popen._tls, 'process_handle', None) is not None

这里，如果Popen._tls对象具有process_handle属性，则thread_is_spawning返回True 。 我们可以看到process_handle属性是在__init__创建的，然后我们想要继承的数据使用dump从父级传递给子级，然后删除该属性。 因此，在__init__期间， thread_is_spawning只会为True 。 根据这个python-ideas邮件列表线程 ，这实际上是一个人为限制，用于模拟与Linux上的os.fork相同的行为。 Windows实际上可以随时支持传递Lock ，因为DuplicateHandle可以随时运行。

以上所有内容都适用于Queue对象，因为它在内部使用Lock 。

我会说继承Lock对象比使用Manager.Lock()更好，因为当你使用Manager.Lock ，你对Lock所做的每一次调用都必须通过IPC发送到Manager进程，这将是比使用调用进程内部的共享Lock慢得多。 但是，这两种方法都是完全有效的。

最后，可以使用initializer / initargs关键字参数将Lock传递给Pool所有成员而不使用Manager ：

lock = None
def initialize_lock(l):
   global lock
   lock = l

def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    """
    lock = mp.Lock()
    mypool = mp.Pool(ncores, initializer=initialize_lock, initargs=(lock,))
    queue = mp.Queue()

    iterator = make_iterator(args, queue)

    mypool.imap(jobfunc, iterator) # Don't pass lock. It has to be used as a global in the child. (This means `jobfunc` would need to be re-written slightly.

    mypool.close()
    mypool.join()

return read_queue(queue)

这是有效的，因为传递给initargs参数会传递给在Pool中运行的Process对象的__init__方法，因此它们最终会被继承而不是被pickle。